Abstract
Current methods for identifying neoplastic cells and discerning them from normal counterparts are often non-specific, slow, biologically perturbing, or a combination, thereof. Here, we show that single-cell, laser-tweezers Raman spectroscopy (LTRS) averts these shortcomings and also permits mechanical manipulation of the single cell under investigation. LTRS is used to characterize the biomolecular Raman signature of both normal human lymphocytes and transformed Jurkat and Raji lymphocyte cell lines from single, unfixed cells in suspension. We demonstrate that single-cell Raman spectra provide a highly reproducible biomolecular fingerprint. Characteristic peaks, mostly due to different DNA and protein concentrations, allow for discerning normal lymphocytes from transformed lymphocytes with high confidence (p << 0.05). Spectra are also compared and analyzed by principal component analysis (PCA) to demonstrate that normal and transformed cells form distinct clusters that can be defined using just two principal components. The method is shown to have a sensitivity of 98.3% for cancer detection, with 97.2% of the cells being correctly classified as being normal or transformed. These results demonstrate the potential application of LTRS as a clinical tool, research instrument, and for cell sorting based on its intrinsic biomolecular signature, therefore eliminating the need for exogenous fluorescent labeling.
Blood Identification at the Single-Cell Level Based on a Combination of Laser Tweezers Raman Spectroscopy and Machine Learning
